Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:01, 8.03MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:12<00:00, 4.82KFile/s]
Downloading celeba: 1.44GB [00:22, 65.6MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fcaa02ab588>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fca9a9ac320>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name = "real_inputs")
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), name = "z_inputs")
    learning_rate = tf.placeholder(tf.float32, name = "learning_rate")
    
    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/usr/local/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/usr/local/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/usr/local/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/usr/local/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/usr/local/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/usr/local/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/usr/local/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/usr/local/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/usr/local/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/usr/local/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/usr/local/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/usr/local/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/usr/local/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/usr/local/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/usr/local/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/usr/local/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/usr/local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/usr/local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/usr/local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-7b5e62e0ea17>", line 22, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/output/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/output/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/output/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/output/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/usr/local/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/usr/local/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/usr/local/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    keep_prob = 0.6
    alpha = 0.3
    
    with tf.variable_scope("discriminator", reuse = reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides = 2, padding = "SAME")
        x1 = tf.nn.dropout(x1, keep_prob)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(x1, 128, 5, strides = 2, padding = "SAME")
        x2 = tf.nn.dropout(x2, keep_prob)
        x2 = tf.layers.batch_normalization(x2, training = True)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d(x2, 256, 5, strides = 2, padding = "SAME")
        x3 = tf.nn.dropout(x3, keep_prob)
        x3 = tf.layers.batch_normalization(x3, training = True)
        x3 = tf.maximum(alpha * x3, x3)
        
        flat = tf.reshape(x3, (-1, 4* 4* 256))
        logits = tf.layers.dense(flat, 1)
        logits = tf.nn.dropout(logits, keep_prob)
        output = tf.sigmoid(logits)
        
    return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.3
    
    with tf.variable_scope("generator", reuse = not is_train):
        x1 = tf.layers.dense(z, 2 * 2 * 512)
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        x1 = tf.layers.batch_normalization(x1, training = is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides = 2, padding = "VALID")
        x2 = tf.layers.batch_normalization(x2, training = is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides = 2, padding = "SAME")
        x3 = tf.layers.batch_normalization(x3, training = is_train)
        x3 = tf.maximum(alpha * x3, x3)        
    
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides = 2, padding = "SAME")
        output = tf.tanh(logits)
        
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse = True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real, labels = tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels = tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake, labels = tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith("discriminator")]
    g_vars = [var for var in t_vars if var.name.startswith("generator")]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(d_loss, var_list = d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(g_loss, var_list = g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [16]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    if(data_image_mode == "L"):
        out_channel_dim = 1
    else:
        out_channel_dim = 3
    
    batches, img_width, img_height, img_depth = data_shape
    input_real, input_z, lr = model_inputs(img_width, img_height, img_depth, z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, img_depth)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    print_every = 10
    show_every = 100
    losses = []
    num_images = 25
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_images *= 2.0
                batch_z = np.random.uniform(-1, 1, (batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict = {input_z: batch_z, input_real: batch_images, lr: learning_rate})
                
                if steps%print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    losses.append((train_loss_d, train_loss_g))
                
                if steps%show_every == 0:
                    show_generator_output(sess, num_images, input_z, img_depth, data_image_mode)        

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [19]:
batch_size = 64
z_dim = 100
learning_rate = 0.002
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/5... Discriminator Loss: 1.5311... Generator Loss: 12.3066
Epoch 1/5... Discriminator Loss: 0.7281... Generator Loss: 2.1330
Epoch 1/5... Discriminator Loss: 2.1110... Generator Loss: 0.4897
Epoch 1/5... Discriminator Loss: 1.4344... Generator Loss: 2.9458
Epoch 1/5... Discriminator Loss: 2.9897... Generator Loss: 4.6810
Epoch 1/5... Discriminator Loss: 1.4262... Generator Loss: 1.1590
Epoch 1/5... Discriminator Loss: 1.4411... Generator Loss: 0.6803
Epoch 1/5... Discriminator Loss: 1.4813... Generator Loss: 0.4902
Epoch 1/5... Discriminator Loss: 2.0140... Generator Loss: 0.4047
Epoch 1/5... Discriminator Loss: 1.6131... Generator Loss: 2.3102
Epoch 1/5... Discriminator Loss: 1.4273... Generator Loss: 0.6876
Epoch 1/5... Discriminator Loss: 2.0267... Generator Loss: 0.4438
Epoch 1/5... Discriminator Loss: 1.8508... Generator Loss: 1.3829
Epoch 1/5... Discriminator Loss: 1.3621... Generator Loss: 1.5831
Epoch 1/5... Discriminator Loss: 1.1270... Generator Loss: 0.7584
Epoch 1/5... Discriminator Loss: 1.5702... Generator Loss: 0.4969
Epoch 1/5... Discriminator Loss: 2.4193... Generator Loss: 2.5621
Epoch 1/5... Discriminator Loss: 2.0012... Generator Loss: 0.4483
Epoch 1/5... Discriminator Loss: 1.5242... Generator Loss: 1.0147
Epoch 1/5... Discriminator Loss: 1.5095... Generator Loss: 0.5386
Epoch 1/5... Discriminator Loss: 1.4916... Generator Loss: 1.5781
Epoch 1/5... Discriminator Loss: 1.5018... Generator Loss: 0.8113
Epoch 1/5... Discriminator Loss: 1.2708... Generator Loss: 0.7926
Epoch 1/5... Discriminator Loss: 1.4916... Generator Loss: 1.2050
Epoch 1/5... Discriminator Loss: 1.5368... Generator Loss: 0.6305
Epoch 1/5... Discriminator Loss: 1.4766... Generator Loss: 0.6707
Epoch 1/5... Discriminator Loss: 1.4245... Generator Loss: 0.9543
Epoch 1/5... Discriminator Loss: 1.3253... Generator Loss: 0.5751
Epoch 1/5... Discriminator Loss: 2.1309... Generator Loss: 2.0152
Epoch 1/5... Discriminator Loss: 1.5256... Generator Loss: 0.8476
Epoch 1/5... Discriminator Loss: 1.6715... Generator Loss: 1.2057
Epoch 1/5... Discriminator Loss: 1.7445... Generator Loss: 1.2316
Epoch 1/5... Discriminator Loss: 1.5745... Generator Loss: 1.4129
Epoch 1/5... Discriminator Loss: 1.2244... Generator Loss: 0.9922
Epoch 1/5... Discriminator Loss: 1.4505... Generator Loss: 0.5374
Epoch 1/5... Discriminator Loss: 1.4243... Generator Loss: 1.2997
Epoch 1/5... Discriminator Loss: 1.3940... Generator Loss: 0.6103
Epoch 1/5... Discriminator Loss: 1.2941... Generator Loss: 0.8483
Epoch 1/5... Discriminator Loss: 1.7363... Generator Loss: 1.7842
Epoch 1/5... Discriminator Loss: 1.3820... Generator Loss: 0.8508
Epoch 1/5... Discriminator Loss: 1.3361... Generator Loss: 0.5064
Epoch 1/5... Discriminator Loss: 1.6340... Generator Loss: 1.2711
Epoch 1/5... Discriminator Loss: 1.5228... Generator Loss: 1.0142
Epoch 1/5... Discriminator Loss: 1.6933... Generator Loss: 0.5950
Epoch 1/5... Discriminator Loss: 1.2762... Generator Loss: 0.7625
Epoch 1/5... Discriminator Loss: 1.4341... Generator Loss: 0.7782
Epoch 1/5... Discriminator Loss: 1.4232... Generator Loss: 0.7051
Epoch 1/5... Discriminator Loss: 1.2500... Generator Loss: 0.8085
Epoch 1/5... Discriminator Loss: 1.3229... Generator Loss: 0.7585
Epoch 1/5... Discriminator Loss: 1.7517... Generator Loss: 1.2560
Epoch 1/5... Discriminator Loss: 1.4018... Generator Loss: 0.8484
Epoch 1/5... Discriminator Loss: 1.7168... Generator Loss: 0.5034
Epoch 1/5... Discriminator Loss: 1.3836... Generator Loss: 1.1523
Epoch 1/5... Discriminator Loss: 1.6134... Generator Loss: 0.6072
Epoch 1/5... Discriminator Loss: 1.4406... Generator Loss: 0.6552
Epoch 1/5... Discriminator Loss: 1.5043... Generator Loss: 0.4647
Epoch 1/5... Discriminator Loss: 1.4264... Generator Loss: 0.7144
Epoch 1/5... Discriminator Loss: 1.4386... Generator Loss: 0.5824
Epoch 1/5... Discriminator Loss: 1.4214... Generator Loss: 0.7881
Epoch 1/5... Discriminator Loss: 1.3191... Generator Loss: 0.9273
Epoch 1/5... Discriminator Loss: 1.5420... Generator Loss: 0.4857
Epoch 1/5... Discriminator Loss: 1.4541... Generator Loss: 0.5788
Epoch 1/5... Discriminator Loss: 1.4776... Generator Loss: 0.9083
Epoch 1/5... Discriminator Loss: 1.7826... Generator Loss: 0.4217
Epoch 1/5... Discriminator Loss: 1.4118... Generator Loss: 0.7885
Epoch 1/5... Discriminator Loss: 1.1854... Generator Loss: 0.5570
Epoch 1/5... Discriminator Loss: 1.3737... Generator Loss: 0.6226
Epoch 1/5... Discriminator Loss: 1.4465... Generator Loss: 0.5330
Epoch 1/5... Discriminator Loss: 1.3600... Generator Loss: 1.0431
Epoch 1/5... Discriminator Loss: 1.4511... Generator Loss: 0.6194
Epoch 1/5... Discriminator Loss: 1.4746... Generator Loss: 0.6931
Epoch 1/5... Discriminator Loss: 1.3993... Generator Loss: 0.7413
Epoch 1/5... Discriminator Loss: 1.5486... Generator Loss: 0.6859
Epoch 1/5... Discriminator Loss: 1.3495... Generator Loss: 0.8362
Epoch 1/5... Discriminator Loss: 1.4456... Generator Loss: 0.6598
Epoch 1/5... Discriminator Loss: 1.4369... Generator Loss: 1.0729
Epoch 1/5... Discriminator Loss: 1.3279... Generator Loss: 0.6927
Epoch 1/5... Discriminator Loss: 1.2358... Generator Loss: 0.5941
Epoch 1/5... Discriminator Loss: 1.4753... Generator Loss: 0.4898
Epoch 1/5... Discriminator Loss: 1.4475... Generator Loss: 0.7087
Epoch 1/5... Discriminator Loss: 1.4950... Generator Loss: 1.1733
Epoch 1/5... Discriminator Loss: 1.4379... Generator Loss: 0.9120
Epoch 1/5... Discriminator Loss: 1.2335... Generator Loss: 0.8410
Epoch 1/5... Discriminator Loss: 1.3485... Generator Loss: 0.6519
Epoch 1/5... Discriminator Loss: 1.3161... Generator Loss: 0.8163
Epoch 1/5... Discriminator Loss: 1.3071... Generator Loss: 0.9692
Epoch 1/5... Discriminator Loss: 1.3485... Generator Loss: 0.7578
Epoch 1/5... Discriminator Loss: 1.4138... Generator Loss: 0.8062
Epoch 1/5... Discriminator Loss: 1.2827... Generator Loss: 0.7282
Epoch 1/5... Discriminator Loss: 1.4578... Generator Loss: 0.5824
Epoch 1/5... Discriminator Loss: 1.4684... Generator Loss: 0.5987
Epoch 1/5... Discriminator Loss: 1.3686... Generator Loss: 0.9369
Epoch 1/5... Discriminator Loss: 1.2226... Generator Loss: 0.9839
Epoch 2/5... Discriminator Loss: 1.4184... Generator Loss: 1.2605
Epoch 2/5... Discriminator Loss: 1.3551... Generator Loss: 0.8574
Epoch 2/5... Discriminator Loss: 1.2369... Generator Loss: 0.8648
Epoch 2/5... Discriminator Loss: 1.4357... Generator Loss: 1.0123
Epoch 2/5... Discriminator Loss: 1.4563... Generator Loss: 0.9345
Epoch 2/5... Discriminator Loss: 1.1437... Generator Loss: 1.1191
Epoch 2/5... Discriminator Loss: 1.3292... Generator Loss: 0.9849
Epoch 2/5... Discriminator Loss: 1.4020... Generator Loss: 0.6803
Epoch 2/5... Discriminator Loss: 1.3480... Generator Loss: 0.7261
Epoch 2/5... Discriminator Loss: 1.4713... Generator Loss: 0.4622
Epoch 2/5... Discriminator Loss: 1.3733... Generator Loss: 0.6400
Epoch 2/5... Discriminator Loss: 1.2795... Generator Loss: 0.7241
Epoch 2/5... Discriminator Loss: 1.4603... Generator Loss: 0.8206
Epoch 2/5... Discriminator Loss: 1.3427... Generator Loss: 0.7817
Epoch 2/5... Discriminator Loss: 1.4059... Generator Loss: 0.5983
Epoch 2/5... Discriminator Loss: 1.5235... Generator Loss: 0.4981
Epoch 2/5... Discriminator Loss: 1.3044... Generator Loss: 1.1229
Epoch 2/5... Discriminator Loss: 1.2361... Generator Loss: 0.7705
Epoch 2/5... Discriminator Loss: 1.4035... Generator Loss: 0.7950
Epoch 2/5... Discriminator Loss: 1.3464... Generator Loss: 0.8318
Epoch 2/5... Discriminator Loss: 1.4528... Generator Loss: 0.6208
Epoch 2/5... Discriminator Loss: 1.4406... Generator Loss: 0.9596
Epoch 2/5... Discriminator Loss: 1.3645... Generator Loss: 0.9023
Epoch 2/5... Discriminator Loss: 1.3844... Generator Loss: 0.7005
Epoch 2/5... Discriminator Loss: 1.4126... Generator Loss: 1.0934
Epoch 2/5... Discriminator Loss: 1.3368... Generator Loss: 0.8072
Epoch 2/5... Discriminator Loss: 1.3812... Generator Loss: 0.8671
Epoch 2/5... Discriminator Loss: 1.3880... Generator Loss: 0.9216
Epoch 2/5... Discriminator Loss: 1.4154... Generator Loss: 1.1076
Epoch 2/5... Discriminator Loss: 1.4588... Generator Loss: 0.6310
Epoch 2/5... Discriminator Loss: 1.5970... Generator Loss: 1.1527
Epoch 2/5... Discriminator Loss: 1.3469... Generator Loss: 0.7211
Epoch 2/5... Discriminator Loss: 1.4457... Generator Loss: 0.6565
Epoch 2/5... Discriminator Loss: 1.2842... Generator Loss: 0.8019
Epoch 2/5... Discriminator Loss: 1.2479... Generator Loss: 0.7080
Epoch 2/5... Discriminator Loss: 1.2017... Generator Loss: 0.7429
Epoch 2/5... Discriminator Loss: 1.2617... Generator Loss: 0.8599
Epoch 2/5... Discriminator Loss: 1.4056... Generator Loss: 1.1166
Epoch 2/5... Discriminator Loss: 1.4814... Generator Loss: 0.6766
Epoch 2/5... Discriminator Loss: 1.3248... Generator Loss: 0.7481
Epoch 2/5... Discriminator Loss: 1.3078... Generator Loss: 0.8447
Epoch 2/5... Discriminator Loss: 1.4808... Generator Loss: 1.4107
Epoch 2/5... Discriminator Loss: 1.4005... Generator Loss: 0.6781
Epoch 2/5... Discriminator Loss: 1.3700... Generator Loss: 0.7573
Epoch 2/5... Discriminator Loss: 1.3333... Generator Loss: 1.0495
Epoch 2/5... Discriminator Loss: 1.7033... Generator Loss: 0.4784
Epoch 2/5... Discriminator Loss: 1.3680... Generator Loss: 0.6060
Epoch 2/5... Discriminator Loss: 1.4374... Generator Loss: 1.0931
Epoch 2/5... Discriminator Loss: 1.3561... Generator Loss: 0.7186
Epoch 2/5... Discriminator Loss: 1.3759... Generator Loss: 0.6880
Epoch 2/5... Discriminator Loss: 1.4085... Generator Loss: 0.9379
Epoch 2/5... Discriminator Loss: 1.3467... Generator Loss: 0.7275
Epoch 2/5... Discriminator Loss: 1.3968... Generator Loss: 0.6017
Epoch 2/5... Discriminator Loss: 1.2631... Generator Loss: 0.7241
Epoch 2/5... Discriminator Loss: 1.3216... Generator Loss: 0.9579
Epoch 2/5... Discriminator Loss: 1.5250... Generator Loss: 0.7061
Epoch 2/5... Discriminator Loss: 1.3893... Generator Loss: 0.7755
Epoch 2/5... Discriminator Loss: 1.3279... Generator Loss: 0.7680
Epoch 2/5... Discriminator Loss: 1.6028... Generator Loss: 1.2258
Epoch 2/5... Discriminator Loss: 1.3597... Generator Loss: 0.6551
Epoch 2/5... Discriminator Loss: 1.2784... Generator Loss: 0.7639
Epoch 2/5... Discriminator Loss: 1.1929... Generator Loss: 0.7790
Epoch 2/5... Discriminator Loss: 1.3930... Generator Loss: 1.0825
Epoch 2/5... Discriminator Loss: 1.4427... Generator Loss: 1.0400
Epoch 2/5... Discriminator Loss: 1.5682... Generator Loss: 0.4890
Epoch 2/5... Discriminator Loss: 1.3364... Generator Loss: 0.6244
Epoch 2/5... Discriminator Loss: 1.2180... Generator Loss: 0.8162
Epoch 2/5... Discriminator Loss: 1.2192... Generator Loss: 0.8914
Epoch 2/5... Discriminator Loss: 1.4788... Generator Loss: 1.0040
Epoch 2/5... Discriminator Loss: 1.3021... Generator Loss: 0.6478
Epoch 2/5... Discriminator Loss: 1.4006... Generator Loss: 0.6848
Epoch 2/5... Discriminator Loss: 1.4427... Generator Loss: 1.0211
Epoch 2/5... Discriminator Loss: 1.3870... Generator Loss: 0.7854
Epoch 2/5... Discriminator Loss: 1.4602... Generator Loss: 1.0254
Epoch 2/5... Discriminator Loss: 1.3188... Generator Loss: 0.7569
Epoch 2/5... Discriminator Loss: 1.4200... Generator Loss: 0.7236
Epoch 2/5... Discriminator Loss: 1.3669... Generator Loss: 0.8584
Epoch 2/5... Discriminator Loss: 1.4249... Generator Loss: 0.6193
Epoch 2/5... Discriminator Loss: 1.5346... Generator Loss: 0.6144
Epoch 2/5... Discriminator Loss: 1.4754... Generator Loss: 0.7233
Epoch 2/5... Discriminator Loss: 1.3468... Generator Loss: 0.6202
Epoch 2/5... Discriminator Loss: 1.2891... Generator Loss: 0.7465
Epoch 2/5... Discriminator Loss: 1.3435... Generator Loss: 0.9338
Epoch 2/5... Discriminator Loss: 1.3384... Generator Loss: 0.7737
Epoch 2/5... Discriminator Loss: 1.3964... Generator Loss: 1.0045
Epoch 2/5... Discriminator Loss: 1.3496... Generator Loss: 0.6397
Epoch 2/5... Discriminator Loss: 1.5280... Generator Loss: 0.5042
Epoch 2/5... Discriminator Loss: 1.3486... Generator Loss: 0.9666
Epoch 2/5... Discriminator Loss: 1.3404... Generator Loss: 0.7876
Epoch 2/5... Discriminator Loss: 1.2559... Generator Loss: 0.7488
Epoch 2/5... Discriminator Loss: 1.3702... Generator Loss: 0.7694
Epoch 2/5... Discriminator Loss: 1.3028... Generator Loss: 0.7629
Epoch 2/5... Discriminator Loss: 1.4257... Generator Loss: 0.5006
Epoch 2/5... Discriminator Loss: 1.3157... Generator Loss: 0.8203
Epoch 3/5... Discriminator Loss: 1.2992... Generator Loss: 0.7815
Epoch 3/5... Discriminator Loss: 1.4673... Generator Loss: 0.6793
Epoch 3/5... Discriminator Loss: 1.3555... Generator Loss: 0.9674
Epoch 3/5... Discriminator Loss: 1.3291... Generator Loss: 0.8791
Epoch 3/5... Discriminator Loss: 1.3414... Generator Loss: 0.6741
Epoch 3/5... Discriminator Loss: 1.4351... Generator Loss: 0.8022
Epoch 3/5... Discriminator Loss: 1.3305... Generator Loss: 0.8638
Epoch 3/5... Discriminator Loss: 1.3137... Generator Loss: 0.5641
Epoch 3/5... Discriminator Loss: 1.3978... Generator Loss: 0.9455
Epoch 3/5... Discriminator Loss: 1.3865... Generator Loss: 0.6339
Epoch 3/5... Discriminator Loss: 1.2653... Generator Loss: 0.7181
Epoch 3/5... Discriminator Loss: 1.2951... Generator Loss: 0.8912
Epoch 3/5... Discriminator Loss: 1.4183... Generator Loss: 0.6608
Epoch 3/5... Discriminator Loss: 1.3707... Generator Loss: 0.9917
Epoch 3/5... Discriminator Loss: 1.2865... Generator Loss: 0.8501
Epoch 3/5... Discriminator Loss: 1.3413... Generator Loss: 0.9816
Epoch 3/5... Discriminator Loss: 1.4757... Generator Loss: 0.5548
Epoch 3/5... Discriminator Loss: 1.4513... Generator Loss: 0.5630
Epoch 3/5... Discriminator Loss: 1.2615... Generator Loss: 0.6815
Epoch 3/5... Discriminator Loss: 1.2899... Generator Loss: 0.7588
Epoch 3/5... Discriminator Loss: 1.5874... Generator Loss: 1.1687
Epoch 3/5... Discriminator Loss: 1.4663... Generator Loss: 1.0669
Epoch 3/5... Discriminator Loss: 1.4229... Generator Loss: 0.6596
Epoch 3/5... Discriminator Loss: 1.4494... Generator Loss: 1.3871
Epoch 3/5... Discriminator Loss: 1.3714... Generator Loss: 0.7685
Epoch 3/5... Discriminator Loss: 1.3192... Generator Loss: 0.8660
Epoch 3/5... Discriminator Loss: 1.3480... Generator Loss: 1.0072
Epoch 3/5... Discriminator Loss: 1.4595... Generator Loss: 0.9966
Epoch 3/5... Discriminator Loss: 1.4189... Generator Loss: 0.9038
Epoch 3/5... Discriminator Loss: 1.4065... Generator Loss: 0.8780
Epoch 3/5... Discriminator Loss: 1.3830... Generator Loss: 0.6884
Epoch 3/5... Discriminator Loss: 1.2949... Generator Loss: 0.7777
Epoch 3/5... Discriminator Loss: 1.4770... Generator Loss: 0.5723
Epoch 3/5... Discriminator Loss: 1.2916... Generator Loss: 0.8410
Epoch 3/5... Discriminator Loss: 1.3137... Generator Loss: 0.8184
Epoch 3/5... Discriminator Loss: 1.3415... Generator Loss: 0.7711
Epoch 3/5... Discriminator Loss: 1.2649... Generator Loss: 0.8203
Epoch 3/5... Discriminator Loss: 1.4213... Generator Loss: 0.6113
Epoch 3/5... Discriminator Loss: 1.4353... Generator Loss: 0.6754
Epoch 3/5... Discriminator Loss: 1.2981... Generator Loss: 0.6968
Epoch 3/5... Discriminator Loss: 1.3518... Generator Loss: 0.6421
Epoch 3/5... Discriminator Loss: 1.3757... Generator Loss: 1.0258
Epoch 3/5... Discriminator Loss: 1.3143... Generator Loss: 0.6655
Epoch 3/5... Discriminator Loss: 1.2970... Generator Loss: 0.7146
Epoch 3/5... Discriminator Loss: 1.3480... Generator Loss: 0.8234
Epoch 3/5... Discriminator Loss: 1.3461... Generator Loss: 0.5567
Epoch 3/5... Discriminator Loss: 1.2471... Generator Loss: 0.6615
Epoch 3/5... Discriminator Loss: 1.3787... Generator Loss: 0.5418
Epoch 3/5... Discriminator Loss: 1.6850... Generator Loss: 1.2475
Epoch 3/5... Discriminator Loss: 1.3001... Generator Loss: 0.6982
Epoch 3/5... Discriminator Loss: 1.5222... Generator Loss: 0.5278
Epoch 3/5... Discriminator Loss: 1.3363... Generator Loss: 0.7160
Epoch 3/5... Discriminator Loss: 1.3154... Generator Loss: 1.0277
Epoch 3/5... Discriminator Loss: 1.2167... Generator Loss: 0.9568
Epoch 3/5... Discriminator Loss: 1.3778... Generator Loss: 0.5167
Epoch 3/5... Discriminator Loss: 1.3091... Generator Loss: 0.9231
Epoch 3/5... Discriminator Loss: 1.3644... Generator Loss: 0.8930
Epoch 3/5... Discriminator Loss: 1.3614... Generator Loss: 1.0495
Epoch 3/5... Discriminator Loss: 1.3660... Generator Loss: 0.6001
Epoch 3/5... Discriminator Loss: 1.1843... Generator Loss: 0.8218
Epoch 3/5... Discriminator Loss: 1.4103... Generator Loss: 1.0800
Epoch 3/5... Discriminator Loss: 1.4269... Generator Loss: 0.6119
Epoch 3/5... Discriminator Loss: 1.4016... Generator Loss: 0.8659
Epoch 3/5... Discriminator Loss: 1.4114... Generator Loss: 0.9344
Epoch 3/5... Discriminator Loss: 1.3316... Generator Loss: 0.7211
Epoch 3/5... Discriminator Loss: 1.4763... Generator Loss: 1.0222
Epoch 3/5... Discriminator Loss: 1.4109... Generator Loss: 0.9854
Epoch 3/5... Discriminator Loss: 1.4266... Generator Loss: 0.5911
Epoch 3/5... Discriminator Loss: 1.2555... Generator Loss: 0.6096
Epoch 3/5... Discriminator Loss: 1.5363... Generator Loss: 0.4710
Epoch 3/5... Discriminator Loss: 1.3474... Generator Loss: 0.6923
Epoch 3/5... Discriminator Loss: 1.1807... Generator Loss: 0.9359
Epoch 3/5... Discriminator Loss: 1.3300... Generator Loss: 0.7681
Epoch 3/5... Discriminator Loss: 1.2558... Generator Loss: 0.6848
Epoch 3/5... Discriminator Loss: 1.4338... Generator Loss: 0.5534
Epoch 3/5... Discriminator Loss: 1.3085... Generator Loss: 0.6881
Epoch 3/5... Discriminator Loss: 1.3510... Generator Loss: 0.8056
Epoch 3/5... Discriminator Loss: 1.3458... Generator Loss: 0.6488
Epoch 3/5... Discriminator Loss: 1.2832... Generator Loss: 0.9148
Epoch 3/5... Discriminator Loss: 1.4403... Generator Loss: 0.5418
Epoch 3/5... Discriminator Loss: 1.6612... Generator Loss: 1.5196
Epoch 3/5... Discriminator Loss: 1.3158... Generator Loss: 0.8929
Epoch 3/5... Discriminator Loss: 1.3901... Generator Loss: 0.8729
Epoch 3/5... Discriminator Loss: 1.4451... Generator Loss: 0.8411
Epoch 3/5... Discriminator Loss: 1.2987... Generator Loss: 0.9143
Epoch 3/5... Discriminator Loss: 1.2281... Generator Loss: 0.7495
Epoch 3/5... Discriminator Loss: 1.2739... Generator Loss: 1.0329
Epoch 3/5... Discriminator Loss: 1.3188... Generator Loss: 0.6789
Epoch 3/5... Discriminator Loss: 1.3570... Generator Loss: 0.7096
Epoch 3/5... Discriminator Loss: 1.5638... Generator Loss: 0.5163
Epoch 3/5... Discriminator Loss: 1.3832... Generator Loss: 0.6002
Epoch 3/5... Discriminator Loss: 1.2184... Generator Loss: 0.9704
Epoch 3/5... Discriminator Loss: 1.2207... Generator Loss: 0.7289
Epoch 3/5... Discriminator Loss: 1.3426... Generator Loss: 1.1212
Epoch 4/5... Discriminator Loss: 1.2967... Generator Loss: 0.9609
Epoch 4/5... Discriminator Loss: 1.6272... Generator Loss: 1.2759
Epoch 4/5... Discriminator Loss: 1.3723... Generator Loss: 0.8242
Epoch 4/5... Discriminator Loss: 1.3544... Generator Loss: 0.6050
Epoch 4/5... Discriminator Loss: 1.2613... Generator Loss: 0.7596
Epoch 4/5... Discriminator Loss: 1.3647... Generator Loss: 0.6126
Epoch 4/5... Discriminator Loss: 1.4653... Generator Loss: 0.5482
Epoch 4/5... Discriminator Loss: 1.2095... Generator Loss: 0.8694
Epoch 4/5... Discriminator Loss: 1.2837... Generator Loss: 0.6507
Epoch 4/5... Discriminator Loss: 1.3867... Generator Loss: 1.0718
Epoch 4/5... Discriminator Loss: 1.2834... Generator Loss: 1.1317
Epoch 4/5... Discriminator Loss: 1.2547... Generator Loss: 0.8229
Epoch 4/5... Discriminator Loss: 1.2199... Generator Loss: 0.8874
Epoch 4/5... Discriminator Loss: 1.4424... Generator Loss: 0.7069
Epoch 4/5... Discriminator Loss: 1.2008... Generator Loss: 0.8681
Epoch 4/5... Discriminator Loss: 1.1689... Generator Loss: 0.7779
Epoch 4/5... Discriminator Loss: 1.2127... Generator Loss: 0.8889
Epoch 4/5... Discriminator Loss: 1.4955... Generator Loss: 1.3228
Epoch 4/5... Discriminator Loss: 1.2758... Generator Loss: 1.0162
Epoch 4/5... Discriminator Loss: 1.2753... Generator Loss: 0.9495
Epoch 4/5... Discriminator Loss: 1.3802... Generator Loss: 0.5970
Epoch 4/5... Discriminator Loss: 1.2048... Generator Loss: 0.5929
Epoch 4/5... Discriminator Loss: 1.4557... Generator Loss: 1.3088
Epoch 4/5... Discriminator Loss: 1.3662... Generator Loss: 0.7638
Epoch 4/5... Discriminator Loss: 1.3783... Generator Loss: 0.5636
Epoch 4/5... Discriminator Loss: 1.3475... Generator Loss: 0.7674
Epoch 4/5... Discriminator Loss: 1.3821... Generator Loss: 0.5546
Epoch 4/5... Discriminator Loss: 1.3208... Generator Loss: 0.7256
Epoch 4/5... Discriminator Loss: 1.4255... Generator Loss: 0.6091
Epoch 4/5... Discriminator Loss: 1.0646... Generator Loss: 0.7026
Epoch 4/5... Discriminator Loss: 1.2139... Generator Loss: 0.9593
Epoch 4/5... Discriminator Loss: 1.2826... Generator Loss: 0.6779
Epoch 4/5... Discriminator Loss: 1.2856... Generator Loss: 0.8778
Epoch 4/5... Discriminator Loss: 1.1592... Generator Loss: 1.0557
Epoch 4/5... Discriminator Loss: 1.2234... Generator Loss: 1.0649
Epoch 4/5... Discriminator Loss: 1.2211... Generator Loss: 0.7712
Epoch 4/5... Discriminator Loss: 1.3061... Generator Loss: 0.9806
Epoch 4/5... Discriminator Loss: 1.3936... Generator Loss: 0.6851
Epoch 4/5... Discriminator Loss: 1.4768... Generator Loss: 1.3799
Epoch 4/5... Discriminator Loss: 1.3842... Generator Loss: 0.9935
Epoch 4/5... Discriminator Loss: 1.2793... Generator Loss: 0.8215
Epoch 4/5... Discriminator Loss: 1.3201... Generator Loss: 0.8600
Epoch 4/5... Discriminator Loss: 1.7472... Generator Loss: 0.5168
Epoch 4/5... Discriminator Loss: 1.2446... Generator Loss: 0.7200
Epoch 4/5... Discriminator Loss: 1.3472... Generator Loss: 0.8420
Epoch 4/5... Discriminator Loss: 1.2244... Generator Loss: 1.1454
Epoch 4/5... Discriminator Loss: 1.5292... Generator Loss: 1.9305
Epoch 4/5... Discriminator Loss: 1.5652... Generator Loss: 1.1110
Epoch 4/5... Discriminator Loss: 1.2624... Generator Loss: 0.7178
Epoch 4/5... Discriminator Loss: 1.4294... Generator Loss: 0.5825
Epoch 4/5... Discriminator Loss: 1.3099... Generator Loss: 0.8554
Epoch 4/5... Discriminator Loss: 1.2478... Generator Loss: 0.7580
Epoch 4/5... Discriminator Loss: 1.3093... Generator Loss: 1.1193
Epoch 4/5... Discriminator Loss: 1.2329... Generator Loss: 0.8954
Epoch 4/5... Discriminator Loss: 1.4727... Generator Loss: 1.1937
Epoch 4/5... Discriminator Loss: 1.1977... Generator Loss: 0.8818
Epoch 4/5... Discriminator Loss: 1.2311... Generator Loss: 0.9478
Epoch 4/5... Discriminator Loss: 1.2894... Generator Loss: 0.7018
Epoch 4/5... Discriminator Loss: 1.4200... Generator Loss: 0.6441
Epoch 4/5... Discriminator Loss: 1.2596... Generator Loss: 0.8493
Epoch 4/5... Discriminator Loss: 1.2891... Generator Loss: 0.7207
Epoch 4/5... Discriminator Loss: 1.3167... Generator Loss: 0.7175
Epoch 4/5... Discriminator Loss: 1.2798... Generator Loss: 0.7020
Epoch 4/5... Discriminator Loss: 1.2640... Generator Loss: 0.6452
Epoch 4/5... Discriminator Loss: 1.5383... Generator Loss: 0.4744
Epoch 4/5... Discriminator Loss: 1.2721... Generator Loss: 0.6345
Epoch 4/5... Discriminator Loss: 1.3420... Generator Loss: 0.6775
Epoch 4/5... Discriminator Loss: 1.7000... Generator Loss: 1.8926
Epoch 4/5... Discriminator Loss: 1.2715... Generator Loss: 0.8752
Epoch 4/5... Discriminator Loss: 1.2464... Generator Loss: 0.8223
Epoch 4/5... Discriminator Loss: 1.1972... Generator Loss: 0.9187
Epoch 4/5... Discriminator Loss: 1.2630... Generator Loss: 0.7551
Epoch 4/5... Discriminator Loss: 1.2755... Generator Loss: 0.8816
Epoch 4/5... Discriminator Loss: 1.2987... Generator Loss: 0.6132
Epoch 4/5... Discriminator Loss: 0.9539... Generator Loss: 1.1948
Epoch 4/5... Discriminator Loss: 1.4191... Generator Loss: 0.5304
Epoch 4/5... Discriminator Loss: 1.2084... Generator Loss: 0.9497
Epoch 4/5... Discriminator Loss: 1.3390... Generator Loss: 1.1930
Epoch 4/5... Discriminator Loss: 1.3208... Generator Loss: 0.7905
Epoch 4/5... Discriminator Loss: 1.2919... Generator Loss: 0.5373
Epoch 4/5... Discriminator Loss: 1.2352... Generator Loss: 0.7394
Epoch 4/5... Discriminator Loss: 1.3074... Generator Loss: 0.9867
Epoch 4/5... Discriminator Loss: 1.2996... Generator Loss: 0.7564
Epoch 4/5... Discriminator Loss: 1.1596... Generator Loss: 1.1422
Epoch 4/5... Discriminator Loss: 1.3666... Generator Loss: 0.6105
Epoch 4/5... Discriminator Loss: 1.1304... Generator Loss: 1.0082
Epoch 4/5... Discriminator Loss: 1.2163... Generator Loss: 0.9127
Epoch 4/5... Discriminator Loss: 1.1248... Generator Loss: 0.8350
Epoch 4/5... Discriminator Loss: 1.3194... Generator Loss: 0.6481
Epoch 4/5... Discriminator Loss: 1.3477... Generator Loss: 0.9208
Epoch 4/5... Discriminator Loss: 1.3125... Generator Loss: 0.5995
Epoch 4/5... Discriminator Loss: 1.2085... Generator Loss: 0.7915
Epoch 4/5... Discriminator Loss: 1.3006... Generator Loss: 0.6435
Epoch 5/5... Discriminator Loss: 1.4935... Generator Loss: 0.5265
Epoch 5/5... Discriminator Loss: 1.2348... Generator Loss: 1.1310
Epoch 5/5... Discriminator Loss: 1.1723... Generator Loss: 1.0373
Epoch 5/5... Discriminator Loss: 1.2775... Generator Loss: 0.9478
Epoch 5/5... Discriminator Loss: 1.1869... Generator Loss: 0.8990
Epoch 5/5... Discriminator Loss: 1.2635... Generator Loss: 0.7882
Epoch 5/5... Discriminator Loss: 1.3719... Generator Loss: 0.8061
Epoch 5/5... Discriminator Loss: 1.2224... Generator Loss: 0.7942
Epoch 5/5... Discriminator Loss: 1.3297... Generator Loss: 0.8057
Epoch 5/5... Discriminator Loss: 1.2400... Generator Loss: 1.2911
Epoch 5/5... Discriminator Loss: 1.1502... Generator Loss: 0.8802
Epoch 5/5... Discriminator Loss: 1.2721... Generator Loss: 0.8709
Epoch 5/5... Discriminator Loss: 1.4495... Generator Loss: 0.5430
Epoch 5/5... Discriminator Loss: 1.3284... Generator Loss: 0.8519
Epoch 5/5... Discriminator Loss: 1.3919... Generator Loss: 0.4777
Epoch 5/5... Discriminator Loss: 1.1559... Generator Loss: 0.9644
Epoch 5/5... Discriminator Loss: 1.0728... Generator Loss: 1.3234
Epoch 5/5... Discriminator Loss: 1.6316... Generator Loss: 0.5802
Epoch 5/5... Discriminator Loss: 1.2191... Generator Loss: 0.9388
Epoch 5/5... Discriminator Loss: 1.4677... Generator Loss: 0.5789
Epoch 5/5... Discriminator Loss: 1.2483... Generator Loss: 0.8665
Epoch 5/5... Discriminator Loss: 1.2301... Generator Loss: 1.1848
Epoch 5/5... Discriminator Loss: 1.1904... Generator Loss: 0.6705
Epoch 5/5... Discriminator Loss: 1.2327... Generator Loss: 1.1349
Epoch 5/5... Discriminator Loss: 1.3125... Generator Loss: 1.2171
Epoch 5/5... Discriminator Loss: 1.2397... Generator Loss: 0.8054
Epoch 5/5... Discriminator Loss: 1.3537... Generator Loss: 0.6743
Epoch 5/5... Discriminator Loss: 1.4755... Generator Loss: 0.5105
Epoch 5/5... Discriminator Loss: 1.4737... Generator Loss: 0.5759
Epoch 5/5... Discriminator Loss: 1.4088... Generator Loss: 0.6672
Epoch 5/5... Discriminator Loss: 1.2179... Generator Loss: 1.3713
Epoch 5/5... Discriminator Loss: 1.3136... Generator Loss: 0.5752
Epoch 5/5... Discriminator Loss: 1.3224... Generator Loss: 1.4045
Epoch 5/5... Discriminator Loss: 1.1594... Generator Loss: 0.7891
Epoch 5/5... Discriminator Loss: 1.3754... Generator Loss: 1.2305
Epoch 5/5... Discriminator Loss: 1.3682... Generator Loss: 0.8321
Epoch 5/5... Discriminator Loss: 1.2540... Generator Loss: 0.6993
Epoch 5/5... Discriminator Loss: 1.2775... Generator Loss: 0.7580
Epoch 5/5... Discriminator Loss: 1.2872... Generator Loss: 1.7617
Epoch 5/5... Discriminator Loss: 1.3188... Generator Loss: 0.7312
Epoch 5/5... Discriminator Loss: 1.2826... Generator Loss: 0.9447
Epoch 5/5... Discriminator Loss: 1.0724... Generator Loss: 0.9842
Epoch 5/5... Discriminator Loss: 1.6177... Generator Loss: 1.5864
Epoch 5/5... Discriminator Loss: 1.3801... Generator Loss: 0.6539
Epoch 5/5... Discriminator Loss: 1.0752... Generator Loss: 0.9614
Epoch 5/5... Discriminator Loss: 1.1048... Generator Loss: 0.8793
Epoch 5/5... Discriminator Loss: 1.3124... Generator Loss: 0.5988
Epoch 5/5... Discriminator Loss: 1.0673... Generator Loss: 1.1433
Epoch 5/5... Discriminator Loss: 1.3160... Generator Loss: 1.3121
Epoch 5/5... Discriminator Loss: 1.1911... Generator Loss: 0.9762
Epoch 5/5... Discriminator Loss: 1.2805... Generator Loss: 0.6009
Epoch 5/5... Discriminator Loss: 1.2553... Generator Loss: 0.7433
Epoch 5/5... Discriminator Loss: 1.3062... Generator Loss: 0.6595
Epoch 5/5... Discriminator Loss: 1.1871... Generator Loss: 0.9943
Epoch 5/5... Discriminator Loss: 1.3905... Generator Loss: 1.5608
Epoch 5/5... Discriminator Loss: 1.2298... Generator Loss: 1.2367
Epoch 5/5... Discriminator Loss: 1.1109... Generator Loss: 1.1482
Epoch 5/5... Discriminator Loss: 1.2583... Generator Loss: 0.7803
Epoch 5/5... Discriminator Loss: 1.1203... Generator Loss: 0.9697
Epoch 5/5... Discriminator Loss: 1.7904... Generator Loss: 0.4392
Epoch 5/5... Discriminator Loss: 1.3744... Generator Loss: 0.5917
Epoch 5/5... Discriminator Loss: 1.1850... Generator Loss: 1.0308
Epoch 5/5... Discriminator Loss: 1.1111... Generator Loss: 1.5207
Epoch 5/5... Discriminator Loss: 1.2554... Generator Loss: 0.9661
Epoch 5/5... Discriminator Loss: 0.9813... Generator Loss: 1.0730
Epoch 5/5... Discriminator Loss: 1.4689... Generator Loss: 0.6211
Epoch 5/5... Discriminator Loss: 1.6665... Generator Loss: 1.3346
Epoch 5/5... Discriminator Loss: 1.3086... Generator Loss: 0.7604
Epoch 5/5... Discriminator Loss: 1.1717... Generator Loss: 1.0206
Epoch 5/5... Discriminator Loss: 1.4371... Generator Loss: 0.7301
Epoch 5/5... Discriminator Loss: 1.3904... Generator Loss: 0.6997
Epoch 5/5... Discriminator Loss: 1.1475... Generator Loss: 1.0885
Epoch 5/5... Discriminator Loss: 1.6145... Generator Loss: 0.5229
Epoch 5/5... Discriminator Loss: 1.2893... Generator Loss: 0.7771
Epoch 5/5... Discriminator Loss: 1.3034... Generator Loss: 0.7662
Epoch 5/5... Discriminator Loss: 1.2565... Generator Loss: 0.8312
Epoch 5/5... Discriminator Loss: 1.6343... Generator Loss: 1.1594
Epoch 5/5... Discriminator Loss: 1.6985... Generator Loss: 0.4417
Epoch 5/5... Discriminator Loss: 1.2092... Generator Loss: 1.0880
Epoch 5/5... Discriminator Loss: 1.2693... Generator Loss: 0.9453
Epoch 5/5... Discriminator Loss: 1.2282... Generator Loss: 1.0090
Epoch 5/5... Discriminator Loss: 1.3524... Generator Loss: 1.0859
Epoch 5/5... Discriminator Loss: 1.2848... Generator Loss: 1.0656
Epoch 5/5... Discriminator Loss: 1.1765... Generator Loss: 1.1273
Epoch 5/5... Discriminator Loss: 1.1402... Generator Loss: 0.7714
Epoch 5/5... Discriminator Loss: 1.1009... Generator Loss: 0.9672
Epoch 5/5... Discriminator Loss: 1.2725... Generator Loss: 0.8320
Epoch 5/5... Discriminator Loss: 1.4387... Generator Loss: 0.4914
Epoch 5/5... Discriminator Loss: 1.2863... Generator Loss: 0.8313
Epoch 5/5... Discriminator Loss: 1.2877... Generator Loss: 0.7950
Epoch 5/5... Discriminator Loss: 1.2255... Generator Loss: 1.3008
Epoch 5/5... Discriminator Loss: 1.1014... Generator Loss: 0.8537
Epoch 5/5... Discriminator Loss: 1.1829... Generator Loss: 0.8036
Epoch 5/5... Discriminator Loss: 1.2378... Generator Loss: 0.8135

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [20]:
batch_size = 64
z_dim = 100
learning_rate = 0.002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.0649... Generator Loss: 2.0039
Epoch 1/1... Discriminator Loss: 2.0198... Generator Loss: 4.0330
Epoch 1/1... Discriminator Loss: 1.6646... Generator Loss: 2.5897
Epoch 1/1... Discriminator Loss: 1.5432... Generator Loss: 0.5827
Epoch 1/1... Discriminator Loss: 1.5769... Generator Loss: 0.5275
Epoch 1/1... Discriminator Loss: 1.0937... Generator Loss: 1.2956
Epoch 1/1... Discriminator Loss: 1.1629... Generator Loss: 0.8277
Epoch 1/1... Discriminator Loss: 1.2565... Generator Loss: 0.8798
Epoch 1/1... Discriminator Loss: 1.7818... Generator Loss: 0.4431
Epoch 1/1... Discriminator Loss: 0.9689... Generator Loss: 1.2525
Epoch 1/1... Discriminator Loss: 4.7366... Generator Loss: 5.0301
Epoch 1/1... Discriminator Loss: 1.2134... Generator Loss: 1.2267
Epoch 1/1... Discriminator Loss: 0.9890... Generator Loss: 1.1354
Epoch 1/1... Discriminator Loss: 1.1108... Generator Loss: 0.7936
Epoch 1/1... Discriminator Loss: 1.3967... Generator Loss: 0.5458
Epoch 1/1... Discriminator Loss: 1.1317... Generator Loss: 0.9297
Epoch 1/1... Discriminator Loss: 1.6751... Generator Loss: 0.7708
Epoch 1/1... Discriminator Loss: 0.8556... Generator Loss: 1.3001
Epoch 1/1... Discriminator Loss: 1.1330... Generator Loss: 0.8686
Epoch 1/1... Discriminator Loss: 1.1268... Generator Loss: 0.6467
Epoch 1/1... Discriminator Loss: 1.0441... Generator Loss: 1.7208
Epoch 1/1... Discriminator Loss: 2.3963... Generator Loss: 0.6964
Epoch 1/1... Discriminator Loss: 1.6227... Generator Loss: 0.8245
Epoch 1/1... Discriminator Loss: 1.2842... Generator Loss: 0.7420
Epoch 1/1... Discriminator Loss: 1.0391... Generator Loss: 0.9859
Epoch 1/1... Discriminator Loss: 1.2594... Generator Loss: 1.1594
Epoch 1/1... Discriminator Loss: 1.2909... Generator Loss: 1.1024
Epoch 1/1... Discriminator Loss: 1.5582... Generator Loss: 1.3712
Epoch 1/1... Discriminator Loss: 1.6024... Generator Loss: 0.8792
Epoch 1/1... Discriminator Loss: 1.4009... Generator Loss: 0.8182
Epoch 1/1... Discriminator Loss: 1.7930... Generator Loss: 0.5458
Epoch 1/1... Discriminator Loss: 1.5791... Generator Loss: 0.7383
Epoch 1/1... Discriminator Loss: 1.3110... Generator Loss: 0.6274
Epoch 1/1... Discriminator Loss: 1.5069... Generator Loss: 0.7122
Epoch 1/1... Discriminator Loss: 1.3847... Generator Loss: 1.0856
Epoch 1/1... Discriminator Loss: 2.0636... Generator Loss: 1.0077
Epoch 1/1... Discriminator Loss: 1.4906... Generator Loss: 0.7083
Epoch 1/1... Discriminator Loss: 1.5974... Generator Loss: 0.8078
Epoch 1/1... Discriminator Loss: 1.4382... Generator Loss: 0.7511
Epoch 1/1... Discriminator Loss: 1.3745... Generator Loss: 0.6307
Epoch 1/1... Discriminator Loss: 1.4894... Generator Loss: 1.0798
Epoch 1/1... Discriminator Loss: 1.5465... Generator Loss: 0.7173
Epoch 1/1... Discriminator Loss: 1.3508... Generator Loss: 0.6419
Epoch 1/1... Discriminator Loss: 1.5034... Generator Loss: 0.7662
Epoch 1/1... Discriminator Loss: 1.5620... Generator Loss: 0.8060
Epoch 1/1... Discriminator Loss: 1.3866... Generator Loss: 0.7049
Epoch 1/1... Discriminator Loss: 1.3984... Generator Loss: 0.7506
Epoch 1/1... Discriminator Loss: 1.5527... Generator Loss: 0.6822
Epoch 1/1... Discriminator Loss: 1.5422... Generator Loss: 0.7464
Epoch 1/1... Discriminator Loss: 1.5497... Generator Loss: 0.9588
Epoch 1/1... Discriminator Loss: 1.3924... Generator Loss: 0.6831
Epoch 1/1... Discriminator Loss: 1.5094... Generator Loss: 0.6719
Epoch 1/1... Discriminator Loss: 1.3321... Generator Loss: 0.8136
Epoch 1/1... Discriminator Loss: 1.5562... Generator Loss: 0.6818
Epoch 1/1... Discriminator Loss: 1.5620... Generator Loss: 0.7667
Epoch 1/1... Discriminator Loss: 1.4060... Generator Loss: 0.7430
Epoch 1/1... Discriminator Loss: 1.4896... Generator Loss: 0.7616
Epoch 1/1... Discriminator Loss: 1.4003... Generator Loss: 0.7711
Epoch 1/1... Discriminator Loss: 1.4654... Generator Loss: 0.6729
Epoch 1/1... Discriminator Loss: 1.4162... Generator Loss: 0.7341
Epoch 1/1... Discriminator Loss: 1.4759... Generator Loss: 1.0089
Epoch 1/1... Discriminator Loss: 1.6408... Generator Loss: 0.7636
Epoch 1/1... Discriminator Loss: 1.4619... Generator Loss: 0.6113
Epoch 1/1... Discriminator Loss: 1.4922... Generator Loss: 0.7912
Epoch 1/1... Discriminator Loss: 1.2275... Generator Loss: 0.7438
Epoch 1/1... Discriminator Loss: 1.4623... Generator Loss: 0.6104
Epoch 1/1... Discriminator Loss: 1.5271... Generator Loss: 0.9112
Epoch 1/1... Discriminator Loss: 1.4115... Generator Loss: 0.7900
Epoch 1/1... Discriminator Loss: 1.5041... Generator Loss: 0.7269
Epoch 1/1... Discriminator Loss: 1.5011... Generator Loss: 0.8368
Epoch 1/1... Discriminator Loss: 1.3864... Generator Loss: 0.6149
Epoch 1/1... Discriminator Loss: 1.2896... Generator Loss: 0.7401
Epoch 1/1... Discriminator Loss: 1.7072... Generator Loss: 0.9149
Epoch 1/1... Discriminator Loss: 1.3733... Generator Loss: 1.1044
Epoch 1/1... Discriminator Loss: 1.3342... Generator Loss: 0.8923
Epoch 1/1... Discriminator Loss: 1.3515... Generator Loss: 1.0131
Epoch 1/1... Discriminator Loss: 1.4648... Generator Loss: 1.1195
Epoch 1/1... Discriminator Loss: 1.3128... Generator Loss: 1.0384
Epoch 1/1... Discriminator Loss: 1.4988... Generator Loss: 0.9618
Epoch 1/1... Discriminator Loss: 1.4974... Generator Loss: 0.5661
Epoch 1/1... Discriminator Loss: 1.6234... Generator Loss: 0.5643
Epoch 1/1... Discriminator Loss: 1.5654... Generator Loss: 0.6286
Epoch 1/1... Discriminator Loss: 1.4413... Generator Loss: 0.6824
Epoch 1/1... Discriminator Loss: 1.1714... Generator Loss: 0.6997
Epoch 1/1... Discriminator Loss: 1.5944... Generator Loss: 0.7705
Epoch 1/1... Discriminator Loss: 1.4479... Generator Loss: 0.6239
Epoch 1/1... Discriminator Loss: 1.5061... Generator Loss: 0.7376
Epoch 1/1... Discriminator Loss: 1.3826... Generator Loss: 1.0322
Epoch 1/1... Discriminator Loss: 1.2672... Generator Loss: 0.9490
Epoch 1/1... Discriminator Loss: 1.3518... Generator Loss: 0.6118
Epoch 1/1... Discriminator Loss: 1.3669... Generator Loss: 0.6716
Epoch 1/1... Discriminator Loss: 1.5816... Generator Loss: 0.6456
Epoch 1/1... Discriminator Loss: 1.2838... Generator Loss: 0.8372
Epoch 1/1... Discriminator Loss: 1.7695... Generator Loss: 0.5956
Epoch 1/1... Discriminator Loss: 1.3595... Generator Loss: 0.8005
Epoch 1/1... Discriminator Loss: 1.4654... Generator Loss: 0.7999
Epoch 1/1... Discriminator Loss: 1.4426... Generator Loss: 0.6216
Epoch 1/1... Discriminator Loss: 1.3147... Generator Loss: 0.8079
Epoch 1/1... Discriminator Loss: 1.4286... Generator Loss: 0.7431
Epoch 1/1... Discriminator Loss: 1.4082... Generator Loss: 0.7844
Epoch 1/1... Discriminator Loss: 1.2901... Generator Loss: 0.9001
Epoch 1/1... Discriminator Loss: 1.3440... Generator Loss: 0.6508
Epoch 1/1... Discriminator Loss: 1.3311... Generator Loss: 0.6561
Epoch 1/1... Discriminator Loss: 1.5148... Generator Loss: 0.5932
Epoch 1/1... Discriminator Loss: 1.2330... Generator Loss: 0.7504
Epoch 1/1... Discriminator Loss: 1.4007... Generator Loss: 0.8142
Epoch 1/1... Discriminator Loss: 1.6076... Generator Loss: 0.9926
Epoch 1/1... Discriminator Loss: 1.4573... Generator Loss: 0.7358
Epoch 1/1... Discriminator Loss: 1.3291... Generator Loss: 0.8141
Epoch 1/1... Discriminator Loss: 1.5294... Generator Loss: 1.1091
Epoch 1/1... Discriminator Loss: 1.3022... Generator Loss: 0.6504
Epoch 1/1... Discriminator Loss: 1.3657... Generator Loss: 0.9023
Epoch 1/1... Discriminator Loss: 1.3154... Generator Loss: 0.7566
Epoch 1/1... Discriminator Loss: 1.6239... Generator Loss: 0.4899
Epoch 1/1... Discriminator Loss: 1.4154... Generator Loss: 0.7225
Epoch 1/1... Discriminator Loss: 1.3074... Generator Loss: 0.7011
Epoch 1/1... Discriminator Loss: 1.3873... Generator Loss: 0.5114
Epoch 1/1... Discriminator Loss: 1.4650... Generator Loss: 1.0944
Epoch 1/1... Discriminator Loss: 1.1743... Generator Loss: 0.9465
Epoch 1/1... Discriminator Loss: 1.5929... Generator Loss: 0.4683
Epoch 1/1... Discriminator Loss: 1.2340... Generator Loss: 0.6761
Epoch 1/1... Discriminator Loss: 1.3354... Generator Loss: 0.9580
Epoch 1/1... Discriminator Loss: 1.3770... Generator Loss: 0.5458
Epoch 1/1... Discriminator Loss: 1.5210... Generator Loss: 0.6995
Epoch 1/1... Discriminator Loss: 1.6753... Generator Loss: 1.3466
Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.8159
Epoch 1/1... Discriminator Loss: 1.3040... Generator Loss: 0.6954
Epoch 1/1... Discriminator Loss: 1.4311... Generator Loss: 0.6745
Epoch 1/1... Discriminator Loss: 1.3097... Generator Loss: 0.8644
Epoch 1/1... Discriminator Loss: 1.4236... Generator Loss: 0.7481
Epoch 1/1... Discriminator Loss: 1.4334... Generator Loss: 0.6882
Epoch 1/1... Discriminator Loss: 1.4838... Generator Loss: 0.6224
Epoch 1/1... Discriminator Loss: 1.4288... Generator Loss: 0.5962
Epoch 1/1... Discriminator Loss: 1.3205... Generator Loss: 1.0536
Epoch 1/1... Discriminator Loss: 1.3065... Generator Loss: 0.5883
Epoch 1/1... Discriminator Loss: 1.4621... Generator Loss: 0.7757
Epoch 1/1... Discriminator Loss: 1.3925... Generator Loss: 0.7311
Epoch 1/1... Discriminator Loss: 1.2052... Generator Loss: 0.8594
Epoch 1/1... Discriminator Loss: 1.3599... Generator Loss: 1.0515
Epoch 1/1... Discriminator Loss: 1.5171... Generator Loss: 0.7240
Epoch 1/1... Discriminator Loss: 1.3886... Generator Loss: 0.6904
Epoch 1/1... Discriminator Loss: 1.2966... Generator Loss: 0.9614
Epoch 1/1... Discriminator Loss: 1.3214... Generator Loss: 1.1698
Epoch 1/1... Discriminator Loss: 1.2760... Generator Loss: 0.6233
Epoch 1/1... Discriminator Loss: 1.4651... Generator Loss: 0.5218
Epoch 1/1... Discriminator Loss: 1.4108... Generator Loss: 0.6761
Epoch 1/1... Discriminator Loss: 1.3985... Generator Loss: 0.7777
Epoch 1/1... Discriminator Loss: 1.3654... Generator Loss: 0.8048
Epoch 1/1... Discriminator Loss: 1.4597... Generator Loss: 0.7199
Epoch 1/1... Discriminator Loss: 1.2698... Generator Loss: 0.7237
Epoch 1/1... Discriminator Loss: 1.3741... Generator Loss: 0.6868
Epoch 1/1... Discriminator Loss: 1.2166... Generator Loss: 0.8610
Epoch 1/1... Discriminator Loss: 1.3490... Generator Loss: 0.6928
Epoch 1/1... Discriminator Loss: 1.4841... Generator Loss: 1.0726
Epoch 1/1... Discriminator Loss: 1.7245... Generator Loss: 1.2202
Epoch 1/1... Discriminator Loss: 1.3727... Generator Loss: 0.7558
Epoch 1/1... Discriminator Loss: 1.5242... Generator Loss: 0.7541
Epoch 1/1... Discriminator Loss: 1.4266... Generator Loss: 0.8579
Epoch 1/1... Discriminator Loss: 1.3860... Generator Loss: 0.8089
Epoch 1/1... Discriminator Loss: 1.4999... Generator Loss: 0.7392
Epoch 1/1... Discriminator Loss: 1.3611... Generator Loss: 0.7490
Epoch 1/1... Discriminator Loss: 1.2744... Generator Loss: 0.7741
Epoch 1/1... Discriminator Loss: 1.3884... Generator Loss: 0.9221
Epoch 1/1... Discriminator Loss: 1.2737... Generator Loss: 0.8332
Epoch 1/1... Discriminator Loss: 1.4462... Generator Loss: 0.6628
Epoch 1/1... Discriminator Loss: 1.2773... Generator Loss: 0.7838
Epoch 1/1... Discriminator Loss: 1.5121... Generator Loss: 0.7069
Epoch 1/1... Discriminator Loss: 1.4024... Generator Loss: 0.6932
Epoch 1/1... Discriminator Loss: 1.3720... Generator Loss: 0.9202
Epoch 1/1... Discriminator Loss: 1.4177... Generator Loss: 0.7202
Epoch 1/1... Discriminator Loss: 1.4818... Generator Loss: 0.8675
Epoch 1/1... Discriminator Loss: 1.3049... Generator Loss: 0.8400
Epoch 1/1... Discriminator Loss: 1.3071... Generator Loss: 0.7448
Epoch 1/1... Discriminator Loss: 1.3382... Generator Loss: 0.8105
Epoch 1/1... Discriminator Loss: 1.3786... Generator Loss: 0.7448
Epoch 1/1... Discriminator Loss: 1.3387... Generator Loss: 0.8003
Epoch 1/1... Discriminator Loss: 1.3931... Generator Loss: 0.8408
Epoch 1/1... Discriminator Loss: 1.3245... Generator Loss: 0.8123
Epoch 1/1... Discriminator Loss: 1.3301... Generator Loss: 0.8181
Epoch 1/1... Discriminator Loss: 1.5144... Generator Loss: 0.6237
Epoch 1/1... Discriminator Loss: 1.4374... Generator Loss: 0.8362
Epoch 1/1... Discriminator Loss: 1.3739... Generator Loss: 0.6068
Epoch 1/1... Discriminator Loss: 1.4046... Generator Loss: 0.5599
Epoch 1/1... Discriminator Loss: 1.4586... Generator Loss: 0.7601
Epoch 1/1... Discriminator Loss: 1.3204... Generator Loss: 0.7429
Epoch 1/1... Discriminator Loss: 1.3569... Generator Loss: 0.7847
Epoch 1/1... Discriminator Loss: 1.3269... Generator Loss: 0.7053
Epoch 1/1... Discriminator Loss: 1.7481... Generator Loss: 0.5460
Epoch 1/1... Discriminator Loss: 1.3312... Generator Loss: 0.7414
Epoch 1/1... Discriminator Loss: 1.3277... Generator Loss: 0.7674
Epoch 1/1... Discriminator Loss: 1.3255... Generator Loss: 0.5642
Epoch 1/1... Discriminator Loss: 1.4645... Generator Loss: 0.9898
Epoch 1/1... Discriminator Loss: 1.4412... Generator Loss: 0.6738
Epoch 1/1... Discriminator Loss: 1.5683... Generator Loss: 0.5200
Epoch 1/1... Discriminator Loss: 1.3230... Generator Loss: 0.7164
Epoch 1/1... Discriminator Loss: 1.2927... Generator Loss: 0.6902
Epoch 1/1... Discriminator Loss: 1.3156... Generator Loss: 0.8429
Epoch 1/1... Discriminator Loss: 1.4511... Generator Loss: 0.5671
Epoch 1/1... Discriminator Loss: 1.2038... Generator Loss: 0.8271
Epoch 1/1... Discriminator Loss: 1.2669... Generator Loss: 0.8115
Epoch 1/1... Discriminator Loss: 1.5270... Generator Loss: 0.5466
Epoch 1/1... Discriminator Loss: 1.4435... Generator Loss: 0.9090
Epoch 1/1... Discriminator Loss: 1.3046... Generator Loss: 0.8878
Epoch 1/1... Discriminator Loss: 1.3672... Generator Loss: 0.9757
Epoch 1/1... Discriminator Loss: 1.3806... Generator Loss: 1.0291
Epoch 1/1... Discriminator Loss: 1.2700... Generator Loss: 0.8413
Epoch 1/1... Discriminator Loss: 1.3669... Generator Loss: 1.1183
Epoch 1/1... Discriminator Loss: 1.3438... Generator Loss: 0.8749
Epoch 1/1... Discriminator Loss: 1.4574... Generator Loss: 0.5822
Epoch 1/1... Discriminator Loss: 1.3796... Generator Loss: 1.0424
Epoch 1/1... Discriminator Loss: 1.4217... Generator Loss: 1.0607
Epoch 1/1... Discriminator Loss: 1.5128... Generator Loss: 0.7608
Epoch 1/1... Discriminator Loss: 1.4244... Generator Loss: 0.8758
Epoch 1/1... Discriminator Loss: 1.2429... Generator Loss: 0.7404
Epoch 1/1... Discriminator Loss: 1.4966... Generator Loss: 0.6173
Epoch 1/1... Discriminator Loss: 1.3997... Generator Loss: 0.8048
Epoch 1/1... Discriminator Loss: 1.4209... Generator Loss: 0.6913
Epoch 1/1... Discriminator Loss: 1.3557... Generator Loss: 0.7202
Epoch 1/1... Discriminator Loss: 1.4455... Generator Loss: 0.7902
Epoch 1/1... Discriminator Loss: 1.4021... Generator Loss: 1.0601
Epoch 1/1... Discriminator Loss: 1.4892... Generator Loss: 0.7706
Epoch 1/1... Discriminator Loss: 1.4342... Generator Loss: 0.6371
Epoch 1/1... Discriminator Loss: 1.3120... Generator Loss: 0.6185
Epoch 1/1... Discriminator Loss: 1.3488... Generator Loss: 0.8130
Epoch 1/1... Discriminator Loss: 1.3452... Generator Loss: 1.0510
Epoch 1/1... Discriminator Loss: 1.2715... Generator Loss: 0.8354
Epoch 1/1... Discriminator Loss: 1.3258... Generator Loss: 0.5884
Epoch 1/1... Discriminator Loss: 1.3420... Generator Loss: 0.6974
Epoch 1/1... Discriminator Loss: 1.3461... Generator Loss: 0.7180
Epoch 1/1... Discriminator Loss: 1.4102... Generator Loss: 0.8297
Epoch 1/1... Discriminator Loss: 1.3643... Generator Loss: 0.7944
Epoch 1/1... Discriminator Loss: 1.3557... Generator Loss: 0.6746
Epoch 1/1... Discriminator Loss: 1.5001... Generator Loss: 0.9645
Epoch 1/1... Discriminator Loss: 1.4186... Generator Loss: 1.0262
Epoch 1/1... Discriminator Loss: 1.4044... Generator Loss: 0.6323
Epoch 1/1... Discriminator Loss: 1.3962... Generator Loss: 0.6829
Epoch 1/1... Discriminator Loss: 1.4708... Generator Loss: 0.5696
Epoch 1/1... Discriminator Loss: 1.3662... Generator Loss: 0.6589
Epoch 1/1... Discriminator Loss: 1.2468... Generator Loss: 0.9705
Epoch 1/1... Discriminator Loss: 1.2319... Generator Loss: 0.8079
Epoch 1/1... Discriminator Loss: 1.3660... Generator Loss: 0.5846
Epoch 1/1... Discriminator Loss: 1.3744... Generator Loss: 0.6814
Epoch 1/1... Discriminator Loss: 1.3707... Generator Loss: 0.8035
Epoch 1/1... Discriminator Loss: 1.4768... Generator Loss: 0.6508
Epoch 1/1... Discriminator Loss: 1.3575... Generator Loss: 0.9565
Epoch 1/1... Discriminator Loss: 1.2833... Generator Loss: 0.7933
Epoch 1/1... Discriminator Loss: 1.4340... Generator Loss: 0.5298
Epoch 1/1... Discriminator Loss: 1.4201... Generator Loss: 0.8211
Epoch 1/1... Discriminator Loss: 1.2843... Generator Loss: 0.9815
Epoch 1/1... Discriminator Loss: 1.3982... Generator Loss: 0.9229
Epoch 1/1... Discriminator Loss: 1.3498... Generator Loss: 0.8241
Epoch 1/1... Discriminator Loss: 1.3874... Generator Loss: 0.9643
Epoch 1/1... Discriminator Loss: 1.4286... Generator Loss: 0.7904
Epoch 1/1... Discriminator Loss: 1.4642... Generator Loss: 0.6636
Epoch 1/1... Discriminator Loss: 1.3649... Generator Loss: 0.8184
Epoch 1/1... Discriminator Loss: 1.3265... Generator Loss: 0.8796
Epoch 1/1... Discriminator Loss: 1.3850... Generator Loss: 0.7566
Epoch 1/1... Discriminator Loss: 1.3304... Generator Loss: 0.7375
Epoch 1/1... Discriminator Loss: 1.3847... Generator Loss: 0.8489
Epoch 1/1... Discriminator Loss: 1.3480... Generator Loss: 0.9404
Epoch 1/1... Discriminator Loss: 1.3684... Generator Loss: 0.6264
Epoch 1/1... Discriminator Loss: 1.3297... Generator Loss: 0.7756
Epoch 1/1... Discriminator Loss: 1.4500... Generator Loss: 0.6433
Epoch 1/1... Discriminator Loss: 1.3991... Generator Loss: 0.8074
Epoch 1/1... Discriminator Loss: 1.3553... Generator Loss: 0.6654
Epoch 1/1... Discriminator Loss: 1.3591... Generator Loss: 0.7108
Epoch 1/1... Discriminator Loss: 1.3400... Generator Loss: 0.6691
Epoch 1/1... Discriminator Loss: 1.3647... Generator Loss: 0.5315
Epoch 1/1... Discriminator Loss: 1.4089... Generator Loss: 0.7098
Epoch 1/1... Discriminator Loss: 1.2821... Generator Loss: 0.7399
Epoch 1/1... Discriminator Loss: 1.3007... Generator Loss: 0.8118
Epoch 1/1... Discriminator Loss: 1.3870... Generator Loss: 0.5702
Epoch 1/1... Discriminator Loss: 1.3185... Generator Loss: 0.8572
Epoch 1/1... Discriminator Loss: 1.3622... Generator Loss: 0.6637
Epoch 1/1... Discriminator Loss: 1.3208... Generator Loss: 0.7320
Epoch 1/1... Discriminator Loss: 1.3728... Generator Loss: 0.7950
Epoch 1/1... Discriminator Loss: 1.4003... Generator Loss: 0.6142
Epoch 1/1... Discriminator Loss: 1.4666... Generator Loss: 0.5825
Epoch 1/1... Discriminator Loss: 1.3664... Generator Loss: 0.5659
Epoch 1/1... Discriminator Loss: 1.2959... Generator Loss: 0.7194
Epoch 1/1... Discriminator Loss: 1.4125... Generator Loss: 0.8853
Epoch 1/1... Discriminator Loss: 1.4433... Generator Loss: 0.7046
Epoch 1/1... Discriminator Loss: 1.4344... Generator Loss: 1.1131
Epoch 1/1... Discriminator Loss: 1.6169... Generator Loss: 0.5122
Epoch 1/1... Discriminator Loss: 1.2209... Generator Loss: 0.7919
Epoch 1/1... Discriminator Loss: 1.4313... Generator Loss: 0.5909
Epoch 1/1... Discriminator Loss: 1.4070... Generator Loss: 0.6891
Epoch 1/1... Discriminator Loss: 1.3239... Generator Loss: 0.8607
Epoch 1/1... Discriminator Loss: 1.3149... Generator Loss: 0.7144
Epoch 1/1... Discriminator Loss: 1.2427... Generator Loss: 0.7311
Epoch 1/1... Discriminator Loss: 1.4257... Generator Loss: 0.8174
Epoch 1/1... Discriminator Loss: 1.4020... Generator Loss: 0.6278
Epoch 1/1... Discriminator Loss: 1.3639... Generator Loss: 0.9657
Epoch 1/1... Discriminator Loss: 1.3294... Generator Loss: 0.6727
Epoch 1/1... Discriminator Loss: 1.4964... Generator Loss: 0.5716
Epoch 1/1... Discriminator Loss: 1.3334... Generator Loss: 0.8096
Epoch 1/1... Discriminator Loss: 1.2864... Generator Loss: 0.6844
Epoch 1/1... Discriminator Loss: 1.3808... Generator Loss: 0.6390
Epoch 1/1... Discriminator Loss: 1.4444... Generator Loss: 0.6086
Epoch 1/1... Discriminator Loss: 1.4793... Generator Loss: 0.5706
Epoch 1/1... Discriminator Loss: 1.2925... Generator Loss: 0.7268
Epoch 1/1... Discriminator Loss: 1.2868... Generator Loss: 0.8108
Epoch 1/1... Discriminator Loss: 1.3467... Generator Loss: 0.7341
Epoch 1/1... Discriminator Loss: 1.3041... Generator Loss: 0.9026
Epoch 1/1... Discriminator Loss: 1.3297... Generator Loss: 0.6141
Epoch 1/1... Discriminator Loss: 1.3952... Generator Loss: 0.8841
Epoch 1/1... Discriminator Loss: 1.4192... Generator Loss: 0.6010
Epoch 1/1... Discriminator Loss: 1.3873... Generator Loss: 0.8598
Epoch 1/1... Discriminator Loss: 1.4709... Generator Loss: 0.8514
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.7701
Epoch 1/1... Discriminator Loss: 1.2606... Generator Loss: 0.7417
Epoch 1/1... Discriminator Loss: 1.2588... Generator Loss: 0.7733
Epoch 1/1... Discriminator Loss: 1.4232... Generator Loss: 0.6827
Epoch 1/1... Discriminator Loss: 1.3511... Generator Loss: 0.7588
Epoch 1/1... Discriminator Loss: 1.3723... Generator Loss: 0.5882
Epoch 1/1... Discriminator Loss: 1.3438... Generator Loss: 0.7395

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.